Quantum Programming in Depth – A Quick Review

Teaching the next generation how to program quantum computers is something I’m deeply passionate about. We can pour billions into cutting-edge quantum hardware, but without accessible software and education, those miraculous machines won’t achieve much. What’s the point of a super-powerful quantum processor if hardly anyone knows how to write programs for it? I’m excited by anything that lowers the barrier to entry for quantum programming. In short, advancing hardware is pointless without equally advanced ways to teach people (especially young people) to make use of it.

As soon as Quantum Programming in Depth by Mariia Mykhailova was released (which was just a few hours ago!), I had to dive in. I want to give a shout-out to author and Manning Publications for writing and publishing this ahead of the mass demand (which I’m sure is coming). It feels like they’ve delivered exactly the resource the community needs, before everyone realizes they need it. This forward-thinking release is perfect timing: as quantum computing interest ramps up, we now have a geat guide ready to train the upcoming wave of quantum developers.

A Hands-On, Problem-Driven Guide

Plenty of books introduce the theory of quantum computing, but this one is intensely practical and problem-driven. It doesn't rehash the same old qubit lore and toy examples – it takes you beyond the basics and into real programming. The author guides you through solving actual problems using code. In fact, the book explicitly focuses on “practical quantum problem solving and programming using Q# and Qiskit”. This dual-language approach is one of the first things that impressed me: the material isn’t tied to a single platform or library. Instead, you learn how to tackle a task in both IBM’s Qiskit (Python) and Microsoft’s Q#, giving you a richer, cross-platform perspective on quantum programming.

Here are a few highlights of the things I personally liked and that I think are different than many others:

• Problem-Based Learning: Each chapter revolves around a concrete project or task, not just abstract theory. The author presents a problem of quantum programming and then demonstrates how to solve it step by. This means you’re learning by doing – exactly how developers learn best.
• Beyond “Hello Qubit”: It’s explicitly designed for those who already know the basics of qubits and superposition, and are ready for the next level. In other words, if you’ve had an intro to quantum and you’re asking “what now?”, this book will help you.
• Two Languages, One Goal: The content is presented with working code in both Qiskit and Q# for each example. This simultaneous use of two different quantum programming frameworks very valuable. It makes it easier to see that concepts transcend any single language. (Also, if you ever wondered about the differences in syntax or approach between Qiskit and Q#, this book gives you a clear side-by-side comparison.)
• Software Engineering Mindset: Quantum Programming in Depth treats quantum coding like real-world software development. That means you don’t just write code – you test it (yes, even using common tools like pytest for your quantum programs!), debug it, and consider its performance. The book even has you thinking about how algorithms will scale on future fault-tolerant quantum computers and how to compare them against classical solutions. It’s a very practical, engineering-oriented approach, which is refreshing in a field often dominated by theoretical talk.
• Progressive Challenge (Quantum Katas Style): The learning curve is managed through progressively harder challenges, reminiscent of the author’s well-known “Quantum Katas” project for self-paced learning. Mykhailova is actually the creator of Microsoft’s Quantum Katas (a set of programming exercises for quantum computing), and you can feel that influence. The book’s chapters build on each other, each adding a new layer of difficulty or a new tool in your toolkit. It’s a gentle climb from relatively simple tasks to quite complex projects – so you’re always challenged but never totally lost.

Real Quantum Projects

I appreciate the variety of real-world inspired examples the book covers. The content is organized into three parts, each tackling a different flavor of quantum programming projects:

Part 1 - Foundations as Building Blocks

Early on, you create fundamental building blocks for quantum software. For example, Chapter 2 guides you through writing a library to prepare arbitrary quantum states. Instead of only preparing the usual |0> and |1> or simple superpositions, you learn how to construct any state you need – a crucial skill for advanced algorithms.

In Chapter 3, you go further by implementing custom quantum operations (unitary gates) that aren’t provided out-of-the-box. This teaches you how to break down complex transformations into basic gates.

By the end of Part 1, you’ve essentially built a toolkit that will be reused for more elaborate algorithms.

Part 2 - Extracting Information (Measurements & Phase Estimation)

Part 2 gets you to play with classic challenge in quantum computing: how to get useful information out of a quantum system.

Chapter 4 focuses on measurement techniques and strategies for gleaning insights about quantum states.

Then Chapter 5 tackles analyzing quantum operations, culminating in a deep dive into the phase estimation problem. Phase estimation is one of the most important subroutines in quantum computing (it’s the key to algorithms like Shor’s factoring algorithm, among others). The book doesn’t shy away from it – it walks you through understanding and implementing phase estimation in a practical way. I really appreciated this, because many “intro” books gloss over phase estimation due to its complexity, but Quantum Programming in Depth makes it accessible, showing why it’s useful and how to code it.

Part 3 - End-to-End Quantum Solutions

Part 3 is where all the pieces come together for solving full-scale problems.

Chapter 6 is a gem: it teaches reversible computing, i.e. how to represent and evaluate classical functions on a quantum computer. This is an essential step for many algorithms – basically, turning a classical computation into a quantum oracle that a quantum algorithm can call. The book does a great job explaining how you can take an ordinary classical logic or function and embed it into a quantum circuit (which by nature has to be reversible). This concept is often a stumbling block for newcomers, but here you get to implement it hands-on, demystifying the process.

Next, Chapter 7 introduces the famous Grover’s search algorithm. You’ll learn how Grover’s algorithm finds a needle in a haystack quadratically faster than any classical method, and you’ll implement it in code (in both Q# and Qiskit) to solve simple searches.

But it doesn’t stop at the textbook explanation – Chapter 8 then applies Grover’s algorithm to a realistic scenario: solving the N-Queens puzzle using Grover’s methods! The N-Queens puzzle (placing N queens on a chessboard so none attack each other) is a classic combinatorial problem, and seeing how to formulate it for a quantum algorithm is incredibly instructive. This part was a highlight for me; it moves beyond contrived examples and shows how a quantum approach might tackle an NP-hard puzzle in practice.

And then, in Chapter 9, the book steps back and discusses evaluating the performance of these quantum algorithms. This includes thinking about how they would run on future error-corrected quantum computers and how to compare them against the best classical algorithms for the same task. In other words, it teaches you a balanced perspective: not every quantum algorithm is worth it, and you need to analyze when quantum truly wins out. This capstone chapter was great for developing an intuition about quantum advantage in practical terms.

Throughout these projects, Quantum Programming in Depth consistently emphasizes writing actual code and testing it. Each section usually ends with working code in both frameworks, and often suggestions for additional experiments or tweaks (the “Going Beyond” sections at the end of chapters give you ideas to extend the project on your own, which is awesome for self-learners). By working through these examples, you’re not just reading theory – you’re building a portfolio of quantum code and, more importantly, the mental models of how to approach new quantum problems.

Accessible to Tech Enthusiasts – Without Dumbing Down

One reason I always gave up on writing technical books, and I started a few, was because I never quite knew how to make complex topics accessible without oversimplifying. The author managed to do just that. The tone is friendly and relatable. Complex ideas like phase estimation are introduced in a down-to-earth way, often with analogies or step-by-step reasoning, so a general tech-savvy reader can follow along. But importantly, the book does not dumb things down. It respects the reader’s intelligence and desire to learn deeply. While it avoids unnecessary math formality, it still tackles substantial topics head-on.

For a general tech enthusiast curious about quantum computing, I’d say this book is a great next step after you’ve read an introductory article or played with an online quantum demo. It’s written in a way that if you’re comfortable with standard programming (Python, basic linear algebra), you can jump in and start doing quantum programming without feeling lost in physics jargon. In fact, going through these projects is probably one of the best ways to truly grasp quantum concepts – by implementing and testing them yourself.

And now I'm off to actually follow the book in the code.